Table of contents

  1. Load required libraries
  2. Load data from .json file
    1. Adjust experiment naming and set order
  3. Run data analysis
  4. Visualize output
    1. Depth bar plot
    2. Coverage barplot
    3. Venn diagram
    4. Intra-map scatter
    5. Inter-map scatter
    6. SVM performance
    7. Organelle profiles
    8. PCA plot
    9. Correlation plots

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Load required libraries

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Load data from .json file

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Adjust experiment naming and set order

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Run data analysis

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Visualize output

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Set default layout options

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Depth barplot

Coverage barplot

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Venn diagram

The venn diagram is one of the few plots, that are not generated through plotly. For display in DOMQC the figure is converted to jpeg. Thus, if the vector graphic is required the diagram has to be regenerated.

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Intra-map scatter

This comparison crucially depends on the selection of complexes. The easiest option is to first identify all complexes that have a minimum number of proteins quantified in all experiments and run the analysis on those. From these only redundant complexes should be removed manually (Nup107-160 subcomplex < Nuclear pore complex, Respiratory chain complex I core < Respiratory chain complex I complete), only retaining one of them.

If a specific set of proteins is to be queried, that is not included in the dictionary of complexes, this dictionary can be modified. This option was not used in the paper.

Different modes for plotting are available and specific complexes can be highlighted using the 'highlight' parameter.

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Inter-map scatter

To calculate the profile reproducibility with a different distance metric or aggregtion across replicates, copy the correspoding line from the analysis cell above.

Gain of high reproducibility groups

An analysis we did not include in the paper is how many groups were gained at a certain reproducibility. This is gaugegd by the 70% quantile in DDA (0.0899, n=1934) and retrieving all groups with higher reproducibility than that.

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SVM performance

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Organelle profiles

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PCA plot

This is not the PCA plot shown in the paper, as a separate json collection was used to ensure comparable positions between figures on different datasets.

There is also a pca plot function in the library, but it is more convoluted and the output is the pca plot interface. Therefore we used a more minimial version here.

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Correlation plots